QoE-Driven Wireless Communication Resource Allocation Based on Digital Twin Edge Network

IF 2.3 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Jing Zhao;Yuanmou Chen;Yi Huang
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引用次数: 0

Abstract

As a real-time representation of physical entities in the digital world, digital twin (DT) has been widely used in many industrial fields, which has brought remarkable efficiency improvement and cost reduction. With the evolution of 6G network, the requirements for ultra-low delay and intelligence are gradually improved, and digital twin edge network (DTEN) came into being. DTEN mainly collects real-time information of physical objects through edge nodes such as BSs and APs, and builds dynamic models on demand based on these information, which has the ability of description, diagnosis, prediction and decision-making. Aiming at the traffic management and resource allocation in DTEN, a QoE-driven wireless communication resource allocation method based on DTEN is proposed, and the overall architecture design of DTEN system is completed. By integrating DT and RL technologies, the DTEN system models can be self-improved and dynamically adjusted, and the data-driven resource allocation model can be updated efficiently. The simulation results show that the proposed algorithm guarantees a high level of QoE for users. Compared with Q-Learning algorithm alone, the proposed algorithm can support more users’ access under the same congestion performance and reduce the iteration times of the algorithm by about 70%.
基于数字双子边缘网络的 QoE 驱动型无线通信资源分配
作为物理实体在数字世界中的实时呈现,数字孪生(DT)已被广泛应用于众多工业领域,带来了显著的效率提升和成本降低。随着 6G 网络的演进,对超低时延和智能化的要求逐渐提高,数字孪生边缘网络(DTEN)应运而生。DTEN 主要通过 BS、AP 等边缘节点采集物理对象的实时信息,并基于这些信息按需建立动态模型,具有描述、诊断、预测和决策能力。针对 DTEN 中的流量管理和资源分配问题,提出了一种基于 DTEN 的 QoE 驱动的无线通信资源分配方法,并完成了 DTEN 系统的总体架构设计。通过整合 DT 和 RL 技术,DTEN 系统模型可以自我完善和动态调整,数据驱动的资源分配模型可以高效更新。仿真结果表明,所提出的算法保证了用户高水平的 QoE。与单独的 Q-Learning 算法相比,所提出的算法能在相同的拥塞性能下支持更多用户的访问,并能将算法的迭代次数减少约 70%。
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CiteScore
5.70
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0.00%
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